{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 生成数据集， 看明白即可无需填写代码\n",
    "#### '<font color=\"blue\">+</font>' 从高斯分布采样 (X, Y) ~ N(3, 6, 1, 1, 0).<br>\n",
    "#### '<font color=\"green\">o</font>' 从高斯分布采样  (X, Y) ~ N(6, 3, 1, 1, 0)<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import animation, rc\n",
    "from IPython.display import HTML\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "dot_num = 100\n",
    "x_p = np.random.normal(3., 1, dot_num)\n",
    "y_p = np.random.normal(6., 1, dot_num)\n",
    "y = np.ones(dot_num)\n",
    "C1 = np.array([x_p, y_p, y]).T\n",
    "\n",
    "x_n = np.random.normal(6., 1, dot_num)\n",
    "y_n = np.random.normal(3., 1, dot_num)\n",
    "y = np.zeros(dot_num)\n",
    "C2 = np.array([x_n, y_n, y]).T\n",
    "\n",
    "plt.scatter(C1[:, 0], C1[:, 1], c='b', marker='+')\n",
    "plt.scatter(C2[:, 0], C2[:, 1], c='g', marker='o')\n",
    "\n",
    "data_set = np.concatenate((C1, C2), axis=0)\n",
    "np.random.shuffle(data_set)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建立模型\n",
    "建立模型类，定义loss函数，定义一步梯度下降过程函数\n",
    "\n",
    "填空一：实现sigmoid的交叉熵损失函数(不使用tf内置的loss 函数)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "epsilon = 1e-12\n",
    "class LogisticRegression():\n",
    "    def __init__(self):\n",
    "        self.W = tf.Variable(shape=[2, 1], dtype=tf.float32, \n",
    "            initial_value=tf.random.uniform(shape=[2, 1], minval=-0.1, maxval=0.1))\n",
    "        self.b = tf.Variable(shape=[1], dtype=tf.float32, initial_value=tf.zeros(shape=[1]))\n",
    "        \n",
    "        self.trainable_variables = [self.W, self.b]\n",
    "    @tf.function\n",
    "    def __call__(self, inp):\n",
    "        logits = tf.matmul(inp, self.W) + self.b # shape(N, 1)\n",
    "        pred = tf.nn.sigmoid(logits)\n",
    "        return pred\n",
    "\n",
    "@tf.function\n",
    "def compute_loss(pred, label):\n",
    "    if not isinstance(label, tf.Tensor):\n",
    "        label = tf.constant(label, dtype=tf.float32)\n",
    "    pred = tf.squeeze(pred, axis=1)\n",
    "    '''============================='''\n",
    "    #输入label shape(N,), pred shape(N,)\n",
    "    #输出 losses shape(N,) 每一个样本一个loss\n",
    "    #todo 填空一，实现sigmoid的交叉熵损失函数(不使用tf内置的loss 函数)\n",
    "    '''============================='''\n",
    "    loss = tf.reduce_mean(losses)\n",
    "    \n",
    "    pred = tf.where(pred>0.5, tf.ones_like(pred), tf.zeros_like(pred))\n",
    "    accuracy = tf.reduce_mean(tf.cast(tf.equal(label, pred), dtype=tf.float32))\n",
    "    return loss, accuracy\n",
    "@tf.function\n",
    "def train_one_step(model, optimizer, x, y):\n",
    "    with tf.GradientTape() as tape:\n",
    "        pred = model(x)\n",
    "        loss, accuracy = compute_loss(pred, y)\n",
    "        \n",
    "    grads = tape.gradient(loss, model.trainable_variables)\n",
    "    optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
    "    return loss, accuracy, model.W, model.b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实例化一个模型，进行训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 0.6471\t accuracy: 0.715\n",
      "loss: 0.4717\t accuracy: 0.985\n",
      "loss: 0.3714\t accuracy: 0.985\n",
      "loss: 0.3084\t accuracy: 0.985\n",
      "loss: 0.2658\t accuracy: 0.985\n",
      "loss: 0.2352\t accuracy: 0.985\n",
      "loss: 0.2121\t accuracy: 0.985\n",
      "loss: 0.1941\t accuracy: 0.985\n",
      "loss: 0.1797\t accuracy: 0.985\n",
      "loss: 0.1678\t accuracy: 0.985\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    model = LogisticRegression()\n",
    "    opt = tf.keras.optimizers.SGD(learning_rate=0.01)\n",
    "    x1, x2, y = list(zip(*data_set))\n",
    "    x = list(zip(x1, x2))\n",
    "    animation_fram = []\n",
    "    \n",
    "    for i in range(200):\n",
    "        loss, accuracy, W_opt, b_opt = train_one_step(model, opt, x, y)\n",
    "        animation_fram.append((W_opt.numpy()[0, 0], W_opt.numpy()[1, 0], b_opt.numpy(), loss.numpy()))\n",
    "        if i%20 == 0:\n",
    "            print(f'loss: {loss.numpy():.4}\\t accuracy: {accuracy.numpy():.4}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 结果展示，无需填写代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "f, ax = plt.subplots(figsize=(6,4))\n",
    "f.suptitle('Logistic Regression Example', fontsize=15)\n",
    "plt.ylabel('Y')\n",
    "plt.xlabel('X')\n",
    "ax.set_xlim(0, 10)\n",
    "ax.set_ylim(0, 10)\n",
    "\n",
    "line_d, = ax.plot([], [], label='fit_line')\n",
    "C1_dots, = ax.plot([], [], '+', c='b', label='actual_dots')\n",
    "C2_dots, = ax.plot([], [], 'o', c='g' ,label='actual_dots')\n",
    "\n",
    "\n",
    "frame_text = ax.text(0.02, 0.95,'',horizontalalignment='left',verticalalignment='top', transform=ax.transAxes)\n",
    "# ax.legend()\n",
    "\n",
    "def init():\n",
    "    line_d.set_data([],[])\n",
    "    C1_dots.set_data([],[])\n",
    "    C2_dots.set_data([],[])\n",
    "    return (line_d,) + (C1_dots,) + (C2_dots,)\n",
    "\n",
    "def animate(i):\n",
    "    xx = np.arange(10, step=0.1)\n",
    "    a = animation_fram[i][0]\n",
    "    b = animation_fram[i][1]\n",
    "    c = animation_fram[i][2]\n",
    "    yy = a/-b * xx +c/-b\n",
    "    line_d.set_data(xx, yy)\n",
    "        \n",
    "    C1_dots.set_data(C1[:, 0], C1[:, 1])\n",
    "    C2_dots.set_data(C2[:, 0], C2[:, 1])\n",
    "    \n",
    "    frame_text.set_text('Timestep = %.1d/%.1d\\nLoss = %.3f' % (i, len(animation_fram), animation_fram[i][3]))\n",
    "    \n",
    "    return (line_d,) + (C1_dots,) + (C2_dots,)\n",
    "\n",
    "anim = animation.FuncAnimation(f, animate, init_func=init,\n",
    "                               frames=len(animation_fram), interval=30, blit=True)\n",
    "\n",
    "HTML(anim.to_html5_video())\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
